Advanced Linear Models for Data Science 2: Statistical Linear Models
- 4.6
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Approx. 6 hours to complete
Course Summary
This course covers the theory and application of linear models, including regression and ANOVA. Students will learn how to interpret and communicate the results of linear models, as well as how to handle common issues such as multicollinearity and heteroscedasticity.Key Learning Points
- Learn how to build and interpret linear models for various types of data
- Gain a deeper understanding of regression and ANOVA
- Explore techniques for handling common challenges in linear modeling
Job Positions & Salaries of people who have taken this course might have
- USA: $63,000 - $98,000
- India: INR 4,00,000 - INR 10,00,000
- Spain: €26,000 - €45,000
- USA: $63,000 - $98,000
- India: INR 4,00,000 - INR 10,00,000
- Spain: €26,000 - €45,000
- USA: $50,000 - $80,000
- India: INR 3,00,000 - INR 7,00,000
- Spain: €22,000 - €35,000
- USA: $63,000 - $98,000
- India: INR 4,00,000 - INR 10,00,000
- Spain: €26,000 - €45,000
- USA: $50,000 - $80,000
- India: INR 3,00,000 - INR 7,00,000
- Spain: €22,000 - €35,000
- USA: $72,000 - $115,000
- India: INR 5,00,000 - INR 12,00,000
- Spain: €30,000 - €50,000
Related Topics for further study
Learning Outcomes
- Understand the theory and application of linear models
- Build and interpret linear models for different data types
- Learn techniques for handling common issues in linear modeling
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics
- Familiarity with R programming language
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Applied Linear Models
- Linear Regression and Modeling
- Practical Regression Analysis
Related Education Paths
Notable People in This Field
- Professor of Statistics and Political Science, Columbia University
- Chief Scientist, RStudio
Related Books
Description
Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following:
Outline
- Introduction and expected values
- Introductory video
- Multivariate expected values, the basics
- Expected values, matrix operations
- Multivariate variances and covariances
- Multivariate covariance and variance matrix operations
- Expected values of quadratic forms
- Expected value properties of least squares estimates
- Welcome to the class
- Course textbook
- Introduction to expected values
- Expected Values
- The multivariate normal distribution
- Normals and multivariate normals
- The singular normal distribution
- Normal likelihoods
- Normal conditional distributions
- Introduction to the multivariate normal
- A note on the last quiz question.
- the multivariate normal
- Distributional results
- Chi squared results for quadratic forms
- Confidence intervals for regression coefficients
- F distribution
- Coding example
- Prediction intervals
- Coding example
- Confidence ellipsoids
- Coding example
- Distributional results
- Distributional results
- Residuals
- Residuals distributional results
- Code demonstration
- Leave one out residuals
- Press residuals
- Residuals
- Thanks for taking the course
- Residuals
Summary of User Reviews
Read reviews of Coursera's Linear Models 2 course. Overall rating is positive, with many users particularly impressed with the course content. However, there are some concerns regarding course structure and pace.Key Aspect Users Liked About This Course
Course content is well-structured and informative.Pros from User Reviews
- In-depth coverage of linear models and related concepts.
- Course materials are well-organized and easy to navigate.
- Instructors are knowledgeable and responsive to student queries.
Cons from User Reviews
- Course pace may be too fast for some learners.
- Course structure could be improved to provide more clarity on expectations.
- Lack of hands-on exercises or real-world applications.
Brian Caffo, PhD
4.6 Raiting
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